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Research

My research focuses on turbulent mixing in stratified flows found in our environment.

To uncover their fascinating small-scale physics, I combine experiments and mathematics.

I also developed an interest in the physics of decompression sickness in scuba diving.

Introduction

Since a picture is worth a thousand words, and a video is worth a million, I made a few short (1 to 3 minutes) videos to introduce my research into stratified flows and turbulence, why they're fascinating, and why they're important.

Introductory videos

Introductory videos
What do I study?

What do I study?

01:01
Play Video
Why do I find it fascinating?

Why do I find it fascinating?

02:21
Play Video
Why is it important? (1/2)

Why is it important? (1/2)

02:51
Play Video
Why is it important? (2/2)

Why is it important? (2/2)

02:08
Play Video
Finding Nessie: an artistic view

Finding Nessie: an artistic view

03:44
Play Video

Projects

Below is a list of my main projects (roughly in chronological order), with a dedicated image gallery that emerged from each of them. For more details check out my Publications page.

Project 1. Estimating internal wave turbulent dissipation in the deep ocean

My first physical oceanography paper (Lefauve, Melet & Muller, 2015) based on my MSc research tackled the turbulent dissipation of internal gravity waves generated by tides interacting with the rough seafloor in the deep, stably-stratified ocean (below the thermocline). From the seafloor these waves propagate upwards, transporting their energy, and steepening as they encounter increasing stratification. This can cause them to break turbulently, dissipating kinetic energy, and mixing the surrounding waters in sometimes highly-localised regions. This mixing is important to sustain the deep branch of the large-scale overturning circulation of the oceans.

 

I produced three-dimensional worlwide maps of energy dissipation by combining linear and nonlinear theories (for the wave generation, propagation, and breaking) with three global datasets: small-scale bathymetry spectral data, tidal data (from satellite altimetry) and stratification data (from Argo floats). I showed that more energy was dissipated near mid-ocean ridges in the Southern Hemisphere and that it was dissipated higher up in the water column than previously thought. I also proposed a simple method to include these results in global ocean models. This paper has been cited over 30 times by the fluid dynamics, oceanography, and climate change communities (in at least 12 different journals).

Project 2. Measuring turbulence in a canonical flow with advanced diagnostics and numerics

To better address the ocean mixing challenges revealed by Project 1, my PhD and postdoctoral research have then moved towards a more fundamental and experimental study of stratified turbulence. Until recently, data-rich laboratory experiments were lacking because no diagnostics were capable of measuring three-dimensional, density-dependent, small-scale turbulence, and no laboratory flow could sustain for long time periods the high levels of dissipation found in Nature. I made such experiments possible by applying state-of-the-art diagnostics to a new, highly-dissipative, canonical stratified shear flow, the stratified inclined duct (abbreviated "SID").

 

The new methodology, originally developed in DAMTP by S. B. Dalziel and J. L. Partridge, uses  three high-speed cameras and a fast-scanning laser sheet. It allows to measure the full three-component velocity and density fields in successive two-dimensional planes, which are then combined into volumes (Partridge, Lefauve & Dalziel, 2019). I obtained 16 datasets of unprecedented quality which allowed me to develop the next two projects. Today we are still developing these measurements in the G. K. Batchelor laboratory and keep pushing the boundaries with the latest laser and high-speed camera technology.

With colleagues A. Atoufi and L. Zhu we have now also developed direct numerical simulations (DNS) of the flow in the SID (Zhu et al. 2023), which have been validated by the experiments, showing an excellent agreement which is rare enough in fluid mechanics to be highlighted. Numerical datasets have allowed us to diagnose inacessible variables such as pressure all along the duct (Atoufi et al. 2023). More recently, we have also shown that physics-informed neural networks could help us augment the resolution experimental data and provide new insights (Zhu et al. 2024).